Search Results for author: Anbang Yao

Found 23 papers, 13 papers with code

Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks

1 code implementation ICCV 2021 Yikai Wang, Yi Yang, Fuchun Sun, Anbang Yao

In the low-bit quantization field, training Binary Neural Networks (BNNs) is the extreme solution to ease the deployment of deep models on resource-constrained devices, having the lowest storage cost and significantly cheaper bit-wise operations compared to 32-bit floating-point counterparts.

Quantization

Explicit Connection Distillation

no code implementations1 Jan 2021 Lujun Li, Yikai Wang, Anbang Yao, Yi Qian, Xiao Zhou, Ke He

In this paper, we present Explicit Connection Distillation (ECD), a new KD framework, which addresses the knowledge distillation problem in a novel perspective of bridging dense intermediate feature connections between a student network and its corresponding teacher generated automatically in the training, achieving knowledge transfer goal via direct cross-network layer-to-layer gradients propagation, without need to define complex distillation losses and assume a pre-trained teacher model to be available.

Image Classification Knowledge Distillation +1

Weights Having Stable Signs Are Important: Finding Primary Subnetworks and Kernels to Compress Binary Weight Networks

no code implementations1 Jan 2021 Zhaole Sun, Anbang Yao

Binary Weight Networks (BWNs) have significantly lower computational and memory costs compared to their full-precision counterparts.

Quantization

Knowledge Transfer via Dense Cross-Layer Mutual-Distillation

1 code implementation ECCV 2020 Anbang Yao, Dawei Sun

Knowledge Distillation (KD) based methods adopt the one-way Knowledge Transfer (KT) scheme in which training a lower-capacity student network is guided by a pre-trained high-capacity teacher network.

Knowledge Distillation Representation Learning +1

Resolution Switchable Networks for Runtime Efficient Image Recognition

1 code implementation ECCV 2020 Yikai Wang, Fuchun Sun, Duo Li, Anbang Yao

We propose a general method to train a single convolutional neural network which is capable of switching image resolutions at inference.

Knowledge Distillation Quantization

CASNet: Common Attribute Support Network for image instance and panoptic segmentation

no code implementations17 Jul 2020 Xiaolong Liu, Yuqing Hou, Anbang Yao, Yurong Chen, Keqiang Li

Given the insight that pixels belonging to one instance have one or more common attributes of current instance, we bring up an one-stage instance segmentation network named Common Attribute Support Network (CASNet), which realizes instance segmentation by predicting and clustering common attributes.

Instance Segmentation Object Detection +1

Learning to Learn Parameterized Classification Networks for Scalable Input Images

1 code implementation ECCV 2020 Duo Li, Anbang Yao, Qifeng Chen

To achieve efficient and flexible image classification at runtime, we employ meta learners to generate convolutional weights of main networks for various input scales and maintain privatized Batch Normalization layers per scale.

Classification General Classification +2

PSConv: Squeezing Feature Pyramid into One Compact Poly-Scale Convolutional Layer

1 code implementation ECCV 2020 Duo Li, Anbang Yao, Qifeng Chen

Despite their strong modeling capacities, Convolutional Neural Networks (CNNs) are often scale-sensitive.

Representation Learning

Learning Two-View Correspondences and Geometry Using Order-Aware Network

1 code implementation ICCV 2019 Jiahui Zhang, Dawei Sun, Zixin Luo, Anbang Yao, Lei Zhou, Tianwei Shen, Yurong Chen, Long Quan, Hongen Liao

First, to capture the local context of sparse correspondences, the network clusters unordered input correspondences by learning a soft assignment matrix.

HBONet: Harmonious Bottleneck on Two Orthogonal Dimensions

1 code implementation ICCV 2019 Duo Li, Aojun Zhou, Anbang Yao

MobileNets, a class of top-performing convolutional neural network architectures in terms of accuracy and efficiency trade-off, are increasingly used in many resourceaware vision applications.

Object Detection Person Re-Identification

Efficient Semantic Scene Completion Network with Spatial Group Convolution

1 code implementation ECCV 2018 Jiahui Zhang, Hao Zhao, Anbang Yao, Yurong Chen, Li Zhang, Hongen Liao

We introduce Spatial Group Convolution (SGC) for accelerating the computation of 3D dense prediction tasks.

A Closed-form Solution to Universal Style Transfer

2 code implementations ICCV 2019 Ming Lu, Hao Zhao, Anbang Yao, Yurong Chen, Feng Xu, Li Zhang

Although plenty of methods have been proposed, a theoretical analysis of feature transform is still missing.

Style Transfer

Deeply-supervised Knowledge Synergy

1 code implementation CVPR 2019 Dawei Sun, Anbang Yao, Aojun Zhou, Hao Zhao

Convolutional Neural Networks (CNNs) have become deeper and more complicated compared with the pioneering AlexNet.

General Classification Image Classification

Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neural Networks

no code implementations CVPR 2018 Aojun Zhou, Anbang Yao, Kuan Wang, Yurong Chen

Through explicitly regularizing the loss perturbation and the weight approximation error in an incremental way, we show that such a new optimization method is theoretically reasonable and practically effective.

Quantization

Network Sketching: Exploiting Binary Structure in Deep CNNs

no code implementations CVPR 2017 Yiwen Guo, Anbang Yao, Hao Zhao, Yurong Chen

Convolutional neural networks (CNNs) with deep architectures have substantially advanced the state-of-the-art in computer vision tasks.

Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights

3 code implementations10 Feb 2017 Aojun Zhou, Anbang Yao, Yiwen Guo, Lin Xu, Yurong Chen

The weights in the other group are responsible to compensate for the accuracy loss from the quantization, thus they are the ones to be re-trained.

Quantization

Dynamic Network Surgery for Efficient DNNs

4 code implementations NeurIPS 2016 Yiwen Guo, Anbang Yao, Yurong Chen

In this paper, we propose a novel network compression method called dynamic network surgery, which can remarkably reduce the network complexity by making on-the-fly connection pruning.

HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

no code implementations CVPR 2016 Tao Kong, Anbang Yao, Yurong Chen, Fuchun Sun

Almost all of the current top-performing object detection networks employ region proposals to guide the search for object instances.

Object Detection Region Proposal

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